Benchmark: sorting speed - MeasureThat.net

Script Preparation code:

var a=Array.from({length:100},()=>Math.random())

Tests:

sort
let b = a.sort() b[0]
reduce
a.reduce((a,c) => a < c ? a : c)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
sort
reduce

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 2 years ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/104.0.5112.102 Safari/537.36 OPR/90.0.4480.100

Browser/OS: Opera 90 on Windows

View result in a separate tab

Test name	Executions per second
sort	145403.0 Ops/sec
reduce	4061070.0 Ops/sec

Autogenerated LLM Summary (model llama3.2:3b, generated 2 months ago):

LLMs can make mistakes. Check important info.

**Overview of the Benchmark**

The provided JSON represents a JavaScript microbenchmark test case on MeasureThat.net. The benchmark measures the performance of two different sorting algorithms: `Array.prototype.sort()` and `Array.prototype.reduce()`. The test is designed to compare the execution speeds of these two algorithms.

**What is being tested?**

In this benchmark, the following options are compared:

1. **Array.prototype.sort()**: This method sorts the elements of an array in place, using a stable sorting algorithm that returns the sorted array.
2. **Array.prototype.reduce()**: This method applies a function to each element of an array, accumulating a result.

**Pros and Cons of Different Approaches**

**Sorting Algorithms:**

*   **`Array.prototype.sort()`**:
    *   Pros:
        +   Simple and widely supported implementation
        +   Well-documented and well-understood algorithm
    *   Cons:
        +   May not be suitable for large datasets due to its in-place sorting nature
        +   Can be slower than other algorithms for certain data sets (e.g., partially sorted arrays)
*   **`Array.prototype.reduce()`**:
    *   Pros:
        +   More flexible and efficient than `sort()` for certain use cases
        +   Can handle large datasets without sacrificing performance
    *   Cons:
        +   Requires a well-defined initial value and accumulator function
        +   May be less familiar or understood by all developers

**Performance Considerations:**

When choosing between these algorithms, consider the following:

*   For small to medium-sized datasets, `Array.prototype.sort()` might be sufficient.
*   For large datasets or when performance is critical, `Array.prototype.reduce()` might be a better choice.
*   Be aware of potential issues with stability and edge cases for both algorithms.

**Library Usage**

In this benchmark, the following library is used:

*   **Built-in JavaScript functions**: Both `Array.prototype.sort()` and `Array.prototype.reduce()` are built-in methods that don't require any external libraries.

**Special JavaScript Features or Syntax**

This benchmark doesn't explicitly use any special JavaScript features or syntax. However, keep in mind that certain browsers may support newer features or syntax for these algorithms (e.g., using `BigInt` for large integers).

**Other Alternatives**

If you need to compare different sorting algorithms or explore alternative approaches, consider the following:

*   **External libraries**: There are many third-party libraries available that provide optimized implementations of sorting algorithms.
*   **Native implementations**: Some languages and libraries (e.g., NumPy) offer native sorting functions with better performance for certain use cases.

In summary, this benchmark provides a simple yet informative way to compare the execution speeds of two commonly used JavaScript sorting algorithms. When choosing between these algorithms or exploring alternative approaches, consider factors like dataset size, performance requirements, and stability issues.

Related benchmarks:

sorting speed (version: 0)

Comparing performance of: sort vs reduce

Created: 2 years ago by: Guest

Jump to the latest result

sort

reduce

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 2 months ago):